2026-04-17T10:26:30Zhttps://riubu.ubu.es/oai/requestoai:riubu.ubu.es:10259/72372024-05-14T10:09:05Zcom_10259_6229com_10259_4534com_10259.4_106com_10259_2604com_10259_6231com_10259_4266com_10259_4393com_10259_5086col_10259_6230col_10259_6232col_10259_4394
Repositorio Institucional de la Universidad de Burgos
author
García Rodríguez, Ana
author
García Rodríguez, Sol
author
Granados López, Diego
author
Diez Mediavilla, Montserrat
author
Alonso Tristán, Cristina
2023-01-13T11:25:47Z
2023-01-13T11:25:47Z
2022-03
2076-3417
http://hdl.handle.net/10259/7237
10.3390/app12052372
2076-3417
Four models for predicting Photosynthetically Active Radiation (PAR) were obtained through MultiLinear Regression (MLR) and an Artificial Neural Network (ANN) based on 10 meteorological indices previously selected from a feature selection algorithm. One model was developed for all sky conditions and the other three for clear, partial, and overcast skies, using a sky classification based on the clearness index (kt). The experimental data were recorded in Burgos (Spain) at ten-minute intervals over 23 months between 2019 and 2021. Fits above 0.97 and Root Mean Square Error (RMSE) values below 7.5% were observed. The models developed for clear and overcast sky conditions yielded better results. Application of the models to the seven experimental ground stations that constitute the Surface Radiation Budget Network (SURFRAD) located in different Köppen climatic zones of the USA yielded fitted values higher than 0.98 and RMSE values less than 11% in all cases regardless of the sky type.
eng
Atribución 4.0 Internacional
Photosynthetically active radiation
kt sky classification
ANN
Multilinear regression models
Extension of PAR Models under Local All-Sky Conditions to Different Climatic Zones
info:eu-repo/semantics/article
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URL
https://riubu.ubu.es/bitstream/10259/7237/1/Garcia-as_2022.pdf
File
MD5
12c812014dccea6f32da975854acb99a
3933833
application/pdf
Garcia-as_2022.pdf